Gienger Edwin, Rokisky Justin, Yin Denise, Pogue Elizabeth A, Piloseno Bianca
Research and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory (JHU/APL), Laurel, MD 20723, USA.
Data Brief. 2024 Jul 6;55:110719. doi: 10.1016/j.dib.2024.110719. eCollection 2024 Aug.
Multi-principal element alloys (MPEAs) have been the focus of study and computationally-guided design for two reasons. MPEAs have shown high strengths and, the vast potential compositional space is more efficiently navigated with machine learning. In this article, we present data from 7385 indentation tests performed on 19 different MPEAs. Samples were arc melted, a thermodynamically complex process forming many distinct phases within a sample. The database was generated by performing hundreds of nanoindentation tests on a given sample and registering the location of those indents with local phase compositions measured with energy dispersive spectroscopy (EDS). The database contains the phases formed in the MPEA, the composition at the location of each indent, and the associated hardness (HV) and modulus for each indent. This data allows researchers targeting data-driven design of high strength systems to extract meaningful correlations between alloying composition, the resulting phases, and mechanical properties for future study.
多主元合金(MPEAs)一直是研究和计算引导设计的焦点,原因有两个。MPEAs已显示出高强度,并且利用机器学习能更有效地探索广阔的潜在成分空间。在本文中,我们展示了对19种不同的MPEAs进行的7385次压痕测试的数据。样品通过电弧熔炼制成,这是一个热力学复杂的过程,会在样品中形成许多不同的相。该数据库是通过对给定样品进行数百次纳米压痕测试,并将这些压痕的位置与用能量色散光谱(EDS)测量的局部相组成进行记录而生成的。该数据库包含MPEA中形成的相、每个压痕位置的成分,以及每个压痕的相关硬度(HV)和模量。这些数据使致力于高强度系统数据驱动设计的研究人员能够提取合金成分、所得相和机械性能之间有意义的相关性,以供未来研究。